4.4k citations · 4.6k across the 6 of their papers we have counts for
6 papers
Privileged Information Dropout in Reinforcement Learning
Pierre-Alexandre Kamienny, Kai Arulkumaran, Feryal Behbahani +2
Using privileged information during training can improve the sample efficiency and performance of machine learning systems. This paradigm has been applied to reinforcement learning…
Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control
Marta Sarrico, Kai Arulkumaran, Andrea Agostinelli +2
Deep networks have enabled reinforcement learning to scale to more complex and challenging domains, but these methods typically require large quantities of training data. An altern…
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means
Andrea Agostinelli, Kai Arulkumaran, Marta Sarrico +2
Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches. Using non-/semi-par…
AlphaStar: An Evolutionary Computation Perspective
Kai Arulkumaran, Antoine Cully, Julian Togelius
In January 2019, DeepMind revealed AlphaStar to the world-the first artificial intelligence (AI) system to beat a professional player at the game of StarCraft II-representing a mil…
A Brief Survey of Deep Reinforcement Learning
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage +1
Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual wor…
On denoising autoencoders trained to minimise binary cross-entropy
Antonia Creswell, Kai Arulkumaran, Anil A. Bharath
Denoising autoencoders (DAEs) are powerful deep learning models used for feature extraction, data generation and network pre-training. DAEs consist of an encoder and decoder which…